Electrical motors have an essential role in modern society. From small fans and compressors that heat and cool our homes to industrial motors that drive large-scale manufacturing processes, electric motors power numerous systems in our society. Not surprisingly, unexpected or unplanned failures of motor-driven systems can have harmful consequences which can result in significant costs and major inconveniences. Unexpected failures of motor-driven systems can require emergency repair, often resulting in unexpected expenses and other challenges.
Operational inefficiencies in motor-driven systems are significant contributors to excessive energy consumption and costs associated with these systems. Such factors can include, for example, bearing friction, clogged filters and pipes, drive-system mechanical misalignment, coolant charge deficiency, and so forth. Unfortunately, a large percentage of such inefficiencies go undiscovered until performance degrades to the point of system failure. Yet, by then, it is often too late to avoid the harmful consequences of a system failure.
Traditional trend monitoring is a well-known and valuable tool to facilitate operation, maintenance, and analysis of various important systems, such as electrical motors described. Examples of continuously monitored variables include electrical load voltage, current and power, as well as industrial process temperatures and pressures, to name a few.
A traditional trend monitoring system starts with a source of data, such as a power meter or temperature sensor. The data source is collected and processed locally (physically near the system to be monitored) continuously to create statistic values, such as average value. Time-stamped values for the predefined trend interval is locally stored (also referred to as data logging). Stored trend data values are periodically transmitted from the local monitoring system to an external system for processing (physically near the personnel who analyze the data). The external system thus accumulates long-term trend data. The external system processes long-term trend data as-needed for analysis and reporting.
The output of the data source is locally fed into some form of data collection device, or logger. Data is typically stored as one or more statistical values (average, maximum, etc.) that represent the value of the data source over a predefined time period, or trend interval. A typical interval time period might be 15 minutes, such that 96 statistical values will be stored, transmitted, and analyzed for each day, for each data source. The resulting accumulated data set can be used to analyze data source trends over time.
Data volume associated with traditional trend monitoring quickly becomes very large and challenging, however. In addition, costs associated with data transmission, storage, and analysis can limit application of trend monitoring in many cases. For example, it may not be practical to apply traditional trend monitoring at trend intervals more frequent than 5 minutes because the resulting data volume rapidly becomes too large.
Moreover, traditional trend monitoring is not intended to capture rapid changes in data. Source data changes that occur significantly faster than the predefined trend interval are very difficult, if not impossible, to detect.